




"""simple docstring"""





import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer

def        _UpperCamelCase     (      UpperCamelCase				)      ->      Union[str, Any]:


			"""simple docstring"""







			if (
			    (cp >= 0x4E_00 and cp <= 0x9F_FF)
			    or (cp >= 0x34_00 and cp <= 0x4D_BF)  #
			    or (cp >= 0x2_00_00 and cp <= 0x2_A6_DF)  #
			    or (cp >= 0x2_A7_00 and cp <= 0x2_B7_3F)  #
			    or (cp >= 0x2_B7_40 and cp <= 0x2_B8_1F)  #
			    or (cp >= 0x2_B8_20 and cp <= 0x2_CE_AF)  #
			    or (cp >= 0xF9_00 and cp <= 0xFA_FF)
			    or (cp >= 0x2_F8_00 and cp <= 0x2_FA_1F)  #
			):  #
						return True

			return False

def        _UpperCamelCase     (      UpperCamelCase				)      ->      Union[str, Any]:


			"""simple docstring"""







			for char in word:
						__UpperCAmelCase						:			Union[str, Any]												=       ord(UpperCamelCase				)
						if not _is_chinese_char(UpperCamelCase				):
									return 0
			return 1

def        _UpperCamelCase     (      UpperCamelCase				)      ->      Dict:


			"""simple docstring"""
			__UpperCAmelCase						:			List[str]												=       set()

			for token in tokens:
						__UpperCAmelCase						:			Optional[int]												=       len(UpperCamelCase				) > 1 and is_chinese(UpperCamelCase				)
						if chinese_word:
									word_set.add(UpperCamelCase				)
			__UpperCAmelCase						:			Optional[Any]												=       list(UpperCamelCase				)
			return word_list

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase				)      ->      Optional[int]:


			"""simple docstring"""







			if not chinese_word_set:
						return bert_tokens
			__UpperCAmelCase						:			str												=       max([len(UpperCamelCase				) for w in chinese_word_set]				)

			__UpperCAmelCase						:			Any												=       bert_tokens
			__UpperCAmelCase						,  __UpperCAmelCase						:			Tuple												=       0, len(UpperCamelCase				)
			while start < end:
						__UpperCAmelCase						:			Any												=       True
						if is_chinese(bert_word[start]				):
									__UpperCAmelCase						:			Dict												=       min(end - start					,							UpperCamelCase				)
									for i in range(UpperCamelCase					,							1					,							-1				):
												__UpperCAmelCase						:			int												=       "".join(bert_word[start : start + i]				)
												if whole_word in chinese_word_set:
															for j in range(start + 1					,							start + i				):
																		__UpperCAmelCase						:			List[Any]												=       "##" + bert_word[j]
															__UpperCAmelCase						:			str												=       start + i
															__UpperCAmelCase						:			Any												=       False
															break
						if single_word:
									start += 1
			return bert_word

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase				)      ->      Optional[int]:


			"""simple docstring"""
			__UpperCAmelCase						:			int												=       []

			for i in range(0					,							len(UpperCamelCase				)					,							100				):
						__UpperCAmelCase						:			List[str]												=       ltp_tokenizer.seg(lines[i : i + 100]				)[0]
						__UpperCAmelCase						:			int												=       [get_chinese_word(UpperCamelCase				) for r in res]
						ltp_res.extend(UpperCamelCase				)
			assert len(UpperCamelCase				) == len(UpperCamelCase				)

			__UpperCAmelCase						:			str												=       []
			for i in range(0					,							len(UpperCamelCase				)					,							100				):
						__UpperCAmelCase						:			Tuple												=       bert_tokenizer(lines[i : i + 100]					,							add_special_tokens=UpperCamelCase					,							truncation=UpperCamelCase					,							max_length=512				)
						bert_res.extend(res["input_ids"]				)
			assert len(UpperCamelCase				) == len(UpperCamelCase				)

			__UpperCAmelCase						:			Any												=       []
			for input_ids, chinese_word in zip(UpperCamelCase					,							UpperCamelCase				):
						__UpperCAmelCase						:			List[str]												=       []
						for id in input_ids:
									__UpperCAmelCase						:			int												=       bert_tokenizer._convert_id_to_token(UpperCamelCase				)
									input_tokens.append(UpperCamelCase				)
						__UpperCAmelCase						:			str												=       add_sub_symbol(UpperCamelCase					,							UpperCamelCase				)
						__UpperCAmelCase						:			Dict												=       []
						# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
						for i, token in enumerate(UpperCamelCase				):
									if token[:2] == "##":
												__UpperCAmelCase						:			Optional[int]												=       token[2:]
												# save chinese tokens' pos
												if len(UpperCamelCase				) == 1 and _is_chinese_char(ord(UpperCamelCase				)				):
															ref_id.append(UpperCamelCase				)
						ref_ids.append(UpperCamelCase				)

			assert len(UpperCamelCase				) == len(UpperCamelCase				)

			return ref_ids

def        _UpperCamelCase     (      UpperCamelCase				)      ->      Optional[int]:


			"""simple docstring"""







			with open(args.file_name					,							"r"					,							encoding="utf-8"				) as f:
						__UpperCAmelCase						:			Dict												=       f.readlines()
			__UpperCAmelCase						:			Union[str, Any]												=       [line.strip() for line in data if len(UpperCamelCase				) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
			__UpperCAmelCase						:			Any												=       LTP(args.ltp				)  # faster in GPU device
			__UpperCAmelCase						:			Optional[int]												=       BertTokenizer.from_pretrained(args.bert				)

			__UpperCAmelCase						:			List[str]												=       prepare_ref(UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase				)

			with open(args.save_path					,							"w"					,							encoding="utf-8"				) as f:
						__UpperCAmelCase						:			int												=       [json.dumps(UpperCamelCase				) + "\n" for ref in ref_ids]
						f.writelines(UpperCamelCase				)


if __name__ == "__main__":
					A				=							argparse.ArgumentParser(description="""prepare_chinese_ref""")
					parser.add_argument(
					    """--file_name""",
					    type=str,
					    default="""./resources/chinese-demo.txt""",
					    help="""file need process, same as training data in lm""",
					)
					parser.add_argument(
					    """--ltp""", type=str, default="""./resources/ltp""", help="""resources for LTP tokenizer, usually a path"""
					)
					parser.add_argument("""--bert""", type=str, default="""./resources/robert""", help="""resources for Bert tokenizer""")
					parser.add_argument("""--save_path""", type=str, default="""./resources/ref.txt""", help="""path to save res""")

					A				=							parser.parse_args()
					main(args)








'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

class 	_snake_case(			unittest.TestCase      ):






 def 						_UpperCamelCase (self       :  List[str]    )       ->   str:






    """simple docstring"""


    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()






 @property
 def 						_UpperCamelCase (self       :  int    )       ->   Any:






    """simple docstring"""


    A__       =     1
    A__       =     3
    A__       =     (32, 32)

    A__       =     floats_tensor((batch_size, num_channels) + sizes    ,		rng=random.Random(0    )    ).to(a    )
    return image






 @property
 def 						_UpperCamelCase (self       :  Union[str, Any]    )       ->   Tuple:






    """simple docstring"""


    torch.manual_seed(0    )
    A__       =     UNetaDConditionModel(
        block_out_channels=(32, 32, 64)    ,		layers_per_block=2    ,		sample_size=32    ,		in_channels=7    ,		out_channels=4    ,		down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D')    ,		up_block_types=('CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'UpBlock2D')    ,		cross_attention_dim=32    ,		attention_head_dim=8    ,		use_linear_projection=a    ,		only_cross_attention=(True, True, False)    ,		num_class_embeds=1_00    ,		)
    return model






 @property
 def 						_UpperCamelCase (self       :  Tuple    )       ->   Optional[Any]:






    """simple docstring"""


    torch.manual_seed(0    )
    A__       =     AutoencoderKL(
        block_out_channels=[32, 32, 64]    ,		in_channels=3    ,		out_channels=3    ,		down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D', 'DownEncoderBlock2D']    ,		up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']    ,		latent_channels=4    ,		)
    return model






 @property
 def 						_UpperCamelCase (self       :  str    )       ->   List[str]:






    """simple docstring"""


    torch.manual_seed(0    )
    A__       =     CLIPTextConfig(
        bos_token_id=0    ,		eos_token_id=2    ,		hidden_size=32    ,		intermediate_size=37    ,		layer_norm_eps=1e-05    ,		num_attention_heads=4    ,		num_hidden_layers=5    ,		pad_token_id=1    ,		vocab_size=10_00    ,		hidden_act='gelu'    ,		projection_dim=5_12    ,		)
    return CLIPTextModel(a    )






 def 						_UpperCamelCase (self       :  str    )       ->   List[str]:






    """simple docstring"""


    A__       =     'cpu'  # ensure determinism for the device-dependent torch.Generator
    A__       =     self.dummy_cond_unet_upscale
    A__       =     DDPMScheduler()
    A__       =     DDIMScheduler(prediction_type='v_prediction'    )
    A__       =     self.dummy_vae
    A__       =     self.dummy_text_encoder
    A__       =     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

    A__       =     self.dummy_image.cpu().permute(0    ,		2    ,		3    ,		1    )[0]
    A__       =     Image.fromarray(np.uinta(a    )    ).convert('RGB'    ).resize((64, 64)    )

    # make sure here that pndm scheduler skips prk
    A__       =     StableDiffusionUpscalePipeline(
        unet=a    ,		low_res_scheduler=a    ,		scheduler=a    ,		vae=a    ,		text_encoder=a    ,		tokenizer=a    ,		max_noise_level=3_50    ,		)
    A__       =     sd_pipe.to(a    )
    sd_pipe.set_progress_bar_config(disable=a    )

    A__       =     'A painting of a squirrel eating a burger'
    A__       =     torch.Generator(device=a    ).manual_seed(0    )
    A__       =     sd_pipe(
        [prompt]    ,		image=a    ,		generator=a    ,		guidance_scale=6.0    ,		noise_level=20    ,		num_inference_steps=2    ,		output_type='np'    ,		)

    A__       =     output.images

    A__       =     torch.Generator(device=a    ).manual_seed(0    )
    A__       =     sd_pipe(
        [prompt]    ,		image=a    ,		generator=a    ,		guidance_scale=6.0    ,		noise_level=20    ,		num_inference_steps=2    ,		output_type='np'    ,		return_dict=a    ,		)[0]

    A__       =     image[0, -3:, -3:, -1]
    A__       =     image_from_tuple[0, -3:, -3:, -1]

    A__       =     low_res_image.size[0] * 4
    assert image.shape == (1, expected_height_width, expected_height_width, 3)
    A__       =     np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661]    )

    assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2
    assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1e-2






 def 						_UpperCamelCase (self       :  Optional[int]    )       ->   Optional[int]:






    """simple docstring"""


    A__       =     'cpu'  # ensure determinism for the device-dependent torch.Generator
    A__       =     self.dummy_cond_unet_upscale
    A__       =     DDPMScheduler()
    A__       =     DDIMScheduler(prediction_type='v_prediction'    )
    A__       =     self.dummy_vae
    A__       =     self.dummy_text_encoder
    A__       =     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

    A__       =     self.dummy_image.cpu().permute(0    ,		2    ,		3    ,		1    )[0]
    A__       =     Image.fromarray(np.uinta(a    )    ).convert('RGB'    ).resize((64, 64)    )

    # make sure here that pndm scheduler skips prk
    A__       =     StableDiffusionUpscalePipeline(
        unet=a    ,		low_res_scheduler=a    ,		scheduler=a    ,		vae=a    ,		text_encoder=a    ,		tokenizer=a    ,		max_noise_level=3_50    ,		)
    A__       =     sd_pipe.to(a    )
    sd_pipe.set_progress_bar_config(disable=a    )

    A__       =     'A painting of a squirrel eating a burger'
    A__       =     sd_pipe(
        2 * [prompt]    ,		image=2 * [low_res_image]    ,		guidance_scale=6.0    ,		noise_level=20    ,		num_inference_steps=2    ,		output_type='np'    ,		)
    A__       =     output.images
    assert image.shape[0] == 2

    A__       =     torch.Generator(device=a    ).manual_seed(0    )
    A__       =     sd_pipe(
        [prompt]    ,		image=a    ,		generator=a    ,		num_images_per_prompt=2    ,		guidance_scale=6.0    ,		noise_level=20    ,		num_inference_steps=2    ,		output_type='np'    ,		)
    A__       =     output.images
    assert image.shape[0] == 2






 @unittest.skipIf(torch_device != 'cuda'    ,		'This test requires a GPU'    )
 def 						_UpperCamelCase (self       :  List[Any]    )       ->   Union[str, Any]:






    """simple docstring"""


    A__       =     self.dummy_cond_unet_upscale
    A__       =     DDPMScheduler()
    A__       =     DDIMScheduler(prediction_type='v_prediction'    )
    A__       =     self.dummy_vae
    A__       =     self.dummy_text_encoder
    A__       =     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

    A__       =     self.dummy_image.cpu().permute(0    ,		2    ,		3    ,		1    )[0]
    A__       =     Image.fromarray(np.uinta(a    )    ).convert('RGB'    ).resize((64, 64)    )

    # put models in fp16, except vae as it overflows in fp16
    A__       =     unet.half()
    A__       =     text_encoder.half()

    # make sure here that pndm scheduler skips prk
    A__       =     StableDiffusionUpscalePipeline(
        unet=a    ,		low_res_scheduler=a    ,		scheduler=a    ,		vae=a    ,		text_encoder=a    ,		tokenizer=a    ,		max_noise_level=3_50    ,		)
    A__       =     sd_pipe.to(a    )
    sd_pipe.set_progress_bar_config(disable=a    )

    A__       =     'A painting of a squirrel eating a burger'
    A__       =     torch.manual_seed(0    )
    A__       =     sd_pipe(
        [prompt]    ,		image=a    ,		generator=a    ,		num_inference_steps=2    ,		output_type='np'    ,		).images

    A__       =     low_res_image.size[0] * 4
    assert image.shape == (1, expected_height_width, expected_height_width, 3)




@slow
@require_torch_gpu
class 	_snake_case(			unittest.TestCase      ):






 def 						_UpperCamelCase (self       :  Union[str, Any]    )       ->   Dict:






    """simple docstring"""


    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()






 def 						_UpperCamelCase (self       :  Optional[Any]    )       ->   Optional[int]:






    """simple docstring"""


    A__       =     load_image(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
        '/sd2-upscale/low_res_cat.png'    )
    A__       =     load_numpy(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
        '/upsampled_cat.npy'    )

    A__       =     'stabilityai/stable-diffusion-x4-upscaler'
    A__       =     StableDiffusionUpscalePipeline.from_pretrained(a    )
    pipe.to(a    )
    pipe.set_progress_bar_config(disable=a    )
    pipe.enable_attention_slicing()

    A__       =     'a cat sitting on a park bench'

    A__       =     torch.manual_seed(0    )
    A__       =     pipe(
        prompt=a    ,		image=a    ,		generator=a    ,		output_type='np'    ,		)
    A__       =     output.images[0]

    assert image.shape == (5_12, 5_12, 3)
    assert np.abs(expected_image - image    ).max() < 1e-3






 def 						_UpperCamelCase (self       :  Any    )       ->   str:






    """simple docstring"""


    A__       =     load_image(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
        '/sd2-upscale/low_res_cat.png'    )
    A__       =     load_numpy(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
        '/upsampled_cat_fp16.npy'    )

    A__       =     'stabilityai/stable-diffusion-x4-upscaler'
    A__       =     StableDiffusionUpscalePipeline.from_pretrained(
        a    ,		torch_dtype=torch.floataa    ,		)
    pipe.to(a    )
    pipe.set_progress_bar_config(disable=a    )
    pipe.enable_attention_slicing()

    A__       =     'a cat sitting on a park bench'

    A__       =     torch.manual_seed(0    )
    A__       =     pipe(
        prompt=a    ,		image=a    ,		generator=a    ,		output_type='np'    ,		)
    A__       =     output.images[0]

    assert image.shape == (5_12, 5_12, 3)
    assert np.abs(expected_image - image    ).max() < 5e-1






 def 						_UpperCamelCase (self       :  Dict    )       ->   List[str]:






    """simple docstring"""


    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()
    torch.cuda.reset_peak_memory_stats()

    A__       =     load_image(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
        '/sd2-upscale/low_res_cat.png'    )

    A__       =     'stabilityai/stable-diffusion-x4-upscaler'
    A__       =     StableDiffusionUpscalePipeline.from_pretrained(
        a    ,		torch_dtype=torch.floataa    ,		)
    pipe.to(a    )
    pipe.set_progress_bar_config(disable=a    )
    pipe.enable_attention_slicing(1    )
    pipe.enable_sequential_cpu_offload()

    A__       =     'a cat sitting on a park bench'

    A__       =     torch.manual_seed(0    )
    A__       =     pipe(
        prompt=a    ,		image=a    ,		generator=a    ,		num_inference_steps=5    ,		output_type='np'    ,		)

    A__       =     torch.cuda.max_memory_allocated()
    # make sure that less than 2.9 GB is allocated
    assert mem_bytes < 2.9 * 10**9


